# Replication materials
for Peisker, Hoffmann, Muttarak (2025): Climate news mediates extreme weather effects on climate change concern

## R/

- prep_reg_data.R
  - standardize variables using the standard deviation of the fixed effects residuals following Mummolo & Peterson (2018)
  - center data on FE for use with lm() in mediate.R since the mediation package does not support fixest models
- regressions.R
  - run and export the mediator and outcome models using fixest
  - run and plot placebo tests (Figures S3 and S4) 
- mediate.R
  - rerun models with lm() and perform mediation analysis with mediate() as presented in the lower panels of Table 1 and Table S14
  - perform sensitivity analysis
- descriptives.R
  - plot media trend over time, climate change concern trends over time, and scatter plot of media and concern (Figure 1)
  - plot trends and map of Google web searches for climate change (Figure S2)
  - calculate summary statistics for the mediator and outcome equations (Tables S2 and S3)
  - summarize data availability by country (Table S1)
  - make table of included events (Table S4)
- functions.R
  - various helper functions to make regression formulas from character strings, print regressions tables, tidy the output of mediate, and calculate and visualize cumulative effects of distributed lag models

## Data/

- eb_climate_weekly.RData: Eurobarometer, media, and weather data merged on a weekly panel of NUTS regions
- eb_climate_reg.RData: output of prep_reg_data.R, standardized data for use with feols() and centered data for use with lm()
- final eurobarometer monthly.RData: Eurobarometer data for Figure 1
- events_eu.csv, events_nat.csv: tables of included European and national events
- tab_emm_sources.csv: table of number of sources by outlet type and country
- NUTS_RG_10M_2016_3035.shp: shape file for plotting 

## Tables/

- var_table_weekly.csv: for making Table S2
- var_table_eb.csv: for making Table S3 
